【数据分析与可视化】Python的input和output
【数据分析与可视化】Python的input和output
瑞新
发布于 2020-07-07 20:15:40
发布于 2020-07-07 20:15:40
有点像序列化一个对象
使用pickle序列化numpy array
import pickle
import numpy as np# 创建一维数组
x = np.arange(10)
xarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])# 把x序列化到硬盘上 wb写2进制
f = open('x.pkl','wb')
# 把x的数据给f文件
pickle.dump(x,f)
# 查看目录文件
!ls[35m2to3[m[m [35mlzfgrep[m[m
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[35mlzegrep[m[m 数组与矩阵运算.ipynb# 打开文件 rb读二进制
f = open('x.pkl','rb')
pickle.load(f)array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])Numpy提供的序列化存储
# Numpy提供的序列化存储
np.save('Numpy序列化',x)
!ls[35m2to3[m[m [35mlzfgrep[m[m
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Numpy序列化.npy [31mlzmainfo[m[m
Pandas数据载入与预处理.ipynb [35mlzmore[m[m
Python的input和output.ipynb [31mmacchangeqt[m[m
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[35mlz4cat[m[m [35mzstdcat[m[m
[35mlzcat[m[m [35mzstdmt[m[m
[35mlzcmp[m[m 创建数组.ipynb
[35mlzdiff[m[m 数组与矩阵运算.ipynb
[35mlzegrep[m[mnp.load('Numpy序列化.npy')array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])# 序列化多个（多条数据同时压缩到一个文件内）
y = np.arange(20)
yarray([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19])np.savez('Numpy多序列化',a = x, b = y)
!ls[35m2to3[m[m [35mlzegrep[m[m
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Numpy序列化.npy [31mlzmadec[m[m
Numpy多序列化.npz [31mlzmainfo[m[m
Pandas数据载入与预处理.ipynb [35mlzmore[m[m
Python的input和output.ipynb [31mmacchangeqt[m[m
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[31mlist_instances[m[m [35mxzegrep[m[m
[31mlrelease[m[m [35mxzfgrep[m[m
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[31mlz4[m[m [31mxzmore[m[m
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[35mlz4cat[m[m [35mzstdcat[m[m
[35mlzcat[m[m [35mzstdmt[m[m
[35mlzcmp[m[m 创建数组.ipynb
[35mlzdiff[m[m 数组与矩阵运算.ipynbc = np.load('Numpy多序列化.npz')c['a']array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])c['b']array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19])其他Numpy阅读文档
在这里插入图片描述
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原始发表：2020-04-16 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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